Pandas Jobs

We are conducting a research project in Geospatial Artificial Intelligence and Remote Sensing that supports an academic manuscript submission to journals such as: ISPRS International Journal of Geo-Information IEEE Journal of Selected Topics in Applied Earh Observations and Remote Sensing The system integrates: Satellite image processing Computer vision object detection Large Language Model (LLM) enrichment Retrieval-Augmented Generation (RAG) The engineering system already exists. Your role is to design and execute rigorous ML experiments and evaluation pipelines to support publication-quality results. Infrastructure and deployment will be handled by a DevOps engineer. System Architecture The pipeline is based on the AWS open-source geospatial processing framework: OSML ModelRunner Refer...

I have a collection of datasets that need to be explored from every angle. First, I want a clear descriptive snapshot that tells me what is happening in the data right now. From there, I need predictive models that forecast future trends, and finally prescriptive recommendations that translate those forecasts into concrete, data-driven actions. You are free to move between Excel pivot tables, Python (preferably pandas, scikit-learn, and matplotlib or seaborn), and R (tidyverse, ggplot2, caret) as each step requires. The end goal is a versatile analysis that can power business decisions, support an academic write-up, and improve day-to-day operational efficiency. Please include: • Cleaned and well-documented datasets • Reproducible Excel workbooks, Python scripts or notebo...

I have several years of historical sales records spread across CSV files, Excel workbooks, and a database. I want you to turn that mixed-source information into a clean, consistent dataset, explore it for trends and seasonality, and then build a machine-learning model that can reliably project future sales. Data preparation must cover every stage—handling missing values, normalising numeric fields, and performing any transformations needed to feed the algorithms. Once the data is tidy, run an exploratory analysis so I can clearly see the patterns that drive revenue, especially how previous sales, marketing spend, and seasonal shifts interact. For modelling, I am open to linear regression, Random Forest, ARIMA, or another regression-based technique you can justify in the report. The...

I have a collection of customer reviews that must be transformed into clear, data-driven insight. The raw files might arrive as CSV, Excel, or even straight from a database—the format is flexible—so the first task is to clean and standardise whatever I supply. That means handling empty rows, removing noise (HTML tags, punctuation, stop-words, etc.) and normalising the text. Once the data is tidy I need robust sentiment classification. Please build the pipeline in Python, making sensible use of NLTK and/or TextBlob for tokenisation, lemmatisation and polarity scoring. The reviews span more than one language, but the immediate focus is on the English subset; your code should therefore detect language and process only English for this milestone while staying extensible for future...

I have a collection of customer reviews that must be transformed into clear, data-driven insight. The raw files might arrive as CSV, Excel, or even straight from a database—the format is flexible—so the first task is to clean and standardise whatever I supply. That means handling empty rows, removing noise (HTML tags, punctuation, stop-words, etc.) and normalising the text. Once the data is tidy I need robust sentiment classification. Please build the pipeline in Python, making sensible use of NLTK and/or TextBlob for tokenisation, lemmatisation and polarity scoring. The reviews span more than one language, but the immediate focus is on the English subset; your code should therefore detect language and process only English for this milestone while staying extensible for future...

Python Data Analysis & Machine Learning Project Description: Hi, I need a freelancer to help with a data analysis and machine learning project. The project includes: Data Cleaning & Preprocessing: Handle missing values, remove duplicates, encode categorical data. Prepare the dataset for analysis and ML model training. Data Analysis & Visualization: Analyze the dataset to identify trends, patterns, and insights. Create visualizations using Python libraries (Pandas, Matplotlib, Seaborn). Machine Learning Model Development: Build and train a supervised learning model (KNN, classification, or regression). Evaluate the model performance using metrics like accuracy, precision, recall. Documentation & Report: Provide a brief report of the analysis, model results, and r...

I have a set of time-stamped observations and I need a robust machine-learning solution that can forecast future values with solid accuracy. The end goal is an automated prediction/forecasting model that I can retrain as new data arrives and easily integrate into a larger Python pipeline. Here’s what I need from you: • Exploratory analysis of the raw time-series data, spotting seasonality, trends, and anomalies. • Thoughtful feature engineering (lags, rolling stats, calendar effects, exogenous variables if useful). • Model selection and training – I’m open to traditional approaches such as ARIMA/Prophet as well as more advanced architectures like Gradient Boosting or LSTM; choose what performs best and explain why. • Rigorous evaluation on a ho...

I am looking for an experienced Python developer to build a trading analysis tool for synthetic indices (such as volatility indices). The tool will help analyze market data, test strategies, and provide a live dashboard for monitoring Requirements Data Collection Connect to a trading platform API (example: Deriv or similar). Collect and store historical price data. Ability to update data automatically. Market Analysis Engine The tool should: Analyze price movements. Detect patterns or indicators. Allow custom strategy rules. Backtesting System Test strategies using historical data. Show results such as: Win rate Profit/loss Drawdown Number of trades Live Analysis Run the strategy on live data. Generate signals or alerts when conditions are met. Dashboard A simple web da...

I’m looking to start a Python-based project purely for Personal use and I’m intentionally keeping the brief open so creative developers can pitch ideas that excite both of us. Whether it’s a handy automation script, a data-driven dashboard, a lightweight Flask or Django web app, a web-scraping utility, or even a small game, I’m happy to explore any direction—as long as it showcases clean, well-documented Python code. Because I do not have a strict deadline (No time limit), I prefer quality over speed. Take the time to think through the concept, architecture, and tech stack; then send me a Detailed project proposal that explains: • The core idea and its personal value • Key Python libraries or frameworks you plan to use (e.g., Pandas, Selenium, Fa...

Project Title: MERN Stack Web Application Development Project Description: I am looking for a developer who can build a web application using the MERN Stack (MongoDB, , React.js, Node.js). This project is for academic purposes. Requirements: • Frontend: React.js with responsive UI • Backend: Node.js and • Database: MongoDB • Features: User authentication (login/signup), CRUD operations, basic dashboard • Clean and well-structured code Additional Requirements: • Source code must be provided • Basic documentation of the project • Proper comments in the code Budget: Open for discussion Deadline: 5–7 days If you have experience with MERN Stack development, please place your bid and share your previous work if available.

Title Build Comprehensive Global Movie & TV Metadata Database for Recommendation Engine Project Overview I am building a personal recommendation engine that predicts my rating for movies and TV shows based on a large history of titles I have already rated. To support accurate predictions, I need a global media metadata database that contains rich structured information for movies and TV series. The dataset should combine multiple trusted sources and be designed for machine-learning comparison against my rating history. Scope of Work Build a master dataset containing global movie and TV metadata. This will serve as the candidate pool for prediction models. The database should include titles from: • IMDb official dataset • The Movie Database API • Optional enri...

I am a recent graduate and want a structured skill-development course that takes me from absolute beginner to confident practitioner in data analysis. The program should combine theory with plenty of hands-on practice so I can start applying what I learn right away. What I expect • A clear learning path that begins with fundamental concepts (data types, basic statistics, exploratory analysis) and steadily introduces more advanced topics. • Practical exercises and mini-projects using widely adopted tools such as Excel, SQL and Python (Pandas, NumPy, Matplotlib or similar), so I can build a small portfolio along the way. • Short quizzes or checkpoints after each module to confirm I have grasped the material. • Real-world datasets for practice plus guidance on where...

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I'm seeking a Python expert to develop a credit risk management model, focusing specifically on market data. Key Requirements: - Credit Risk Expertise: In-depth understanding of credit risk, particularly related to market data. - Python Proficiency: Advanced skills in Python for data analysis and modeling. - Data Handling: Experience in working with large datasets, especially market data. - Financial Acumen: Strong background in finance and risk management principles. Ideal Skills and Experience: - Proven experience in developing credit risk models. - Familiarity with relevant Python libraries (e.g., Pandas, NumPy, Scikit-learn). - Ability to provide clear documentation and insights on the model developed. Please share your relevant experience and approach to this project. I look ...

I have already trained and deployed a Logistic Regression model in Streamlit that classifies breast-tumour samples as malignant or benign. What I need now is a polished data-visualization layer so users can quickly grasp how each feature influences the prediction. My immediate focus is on bar-chart visualisations. I want clear, well-labelled bars that compare malignant vs. benign distributions, show feature importances, and surface any other insight you think adds value. The work should plug straight into my current Streamlit app and read from the same Pandas DataFrame I am already passing to the model. Although the main task is visualisation, I am also experimenting with feature selection, so if your code can be structured in a way that makes it easy to toggle feature subsets, that wi...

I want a single Excel file that lets me drop in fresh sales and purchase data and instantly see what’s happening in the business. The heart of the job is an interactive dashboard—clean, professional, and easy for anyone on my team to grasp at a glance. Here is what I need the file to do: • Automatically refresh whenever I upload a new data file, so no manual copy-pasting. • Display bar, line, and pie charts that highlight both sales trends and purchase trends. • Use pivot tables and slicers to let me drill down by product, date, or supplier without breaking the layout. • Track current inventory levels in real time, flagging low-stock items. • Surface the two KPIs I care about most—product-wise performance and overall revenue growth&mdas...

I am looking for a part-time Python developer to build a dynamic financial simulation and data visualization tool. We are currently in a strategic planning phase where we need to model various **exit scenarios** and calculate **profit potential** based on fluctuating market variables. The goal is to move from "uncertain claims" to "data-driven probabilities." ### **Key Responsibilities / Scope of Work** * **Dynamic Modeling:** Create a Python-based simulation engine that accepts multiple inputs (revenue growth, operational costs, market multiples) to project ROI. * **Exit Scenario Analysis:** Build logic to test different exit criteria (e.g., acquisition at Year 3 vs. Year 5) and their impact on terminal value. * **Data Integration:** Fetch and process market benchma...